Retrieving items in online e-commerce systems with abundance of products is time consuming for users. To deal with this issue, recommender systems (RS) aims to help users by suggesting their interested items in the presence of thousands of products. Generally, RS algorithms are constructed based on similarity between users and/or items (e.g., a user is likely to purchase the same items as his/her most similar users). In this paper, we introduce a novel time-aware recommendation algorithm that is based on overlapping community structure between users. Users' interests might change over time, and thus accurate modelling of dynamic users' tastes is a challenging issue in designing efficient recommendation systems. The users-items interaction network is often highly sparse in real systems, for which many recommenders fail to provide accurate predictions. We apply the proposed algorithm on a benchmark dataset. Our proposed recommendation algorithm overcomes these challenges and show better precision as compared to the state-of-the-art recommenders.
